package com.aplink.generic.google.maps.clustering.algo;

import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Set;

import com.aplink.generic.google.maps.clustering.Cluster;
import com.aplink.generic.google.maps.clustering.ClusterItem;
import com.aplink.generic.google.maps.geometry.Bounds;
import com.aplink.generic.google.maps.geometry.Point;
import com.aplink.generic.google.maps.projection.SphericalMercatorProjection;
import com.aplink.generic.google.maps.quadtree.PointQuadTree;
import com.google.android.gms.maps.model.LatLng;

/**
 * A simple clustering algorithm with O(nlog n) performance. Resulting clusters
 * are not hierarchical.
 * <p/>
 * High level algorithm:<br>
 * 1. Iterate over items in the order they were added (candidate clusters).<br>
 * 2. Create a cluster with the center of the item. <br>
 * 3. Add all items that are within a certain distance to the cluster. <br>
 * 4. Move any items out of an existing cluster if they are closer to another
 * cluster. <br>
 * 5. Remove those items from the list of candidate clusters.
 * <p/>
 * Clusters have the center of the first element (not the centroid of the items
 * within it).
 */
public class NonHierarchicalDistanceBasedAlgorithm<T extends ClusterItem>
        implements Algorithm<T> {
    private static class QuadItem<T extends ClusterItem> implements
            PointQuadTree.Item, Cluster<T> {
        private final T mClusterItem;
        private final Point mPoint;
        private final LatLng mPosition;
        private final Set<T> singletonSet;

        private QuadItem(final T item) {
            mClusterItem = item;
            mPosition = item.getPosition();
            mPoint = NonHierarchicalDistanceBasedAlgorithm.PROJECTION
                    .toPoint(mPosition);
            singletonSet = Collections.singleton(mClusterItem);
        }

        @Override
        public Set<T> getItems() {
            return singletonSet;
        }

        @Override
        public Point getPoint() {
            return mPoint;
        }

        @Override
        public LatLng getPosition() {
            return mPosition;
        }

        @Override
        public int getSize() {
            return 1;
        }
    }

    public static final int MAX_DISTANCE_AT_ZOOM = 100; // essentially 100 dp.

    private static final SphericalMercatorProjection PROJECTION = new SphericalMercatorProjection(
            1);

    /**
     * Any modifications should be synchronized on mQuadTree.
     */
    private final Collection<QuadItem<T>> mItems = new ArrayList<QuadItem<T>>();

    /**
     * Any modifications should be synchronized on mQuadTree.
     */
    private final PointQuadTree<QuadItem<T>> mQuadTree = new PointQuadTree<QuadItem<T>>(
            0, 1, 0, 1);

    @Override
    public void addItem(final T item) {
        final QuadItem<T> quadItem = new QuadItem<T>(item);
        synchronized (mQuadTree) {
            mItems.add(quadItem);
            mQuadTree.add(quadItem);
        }
    }

    @Override
    public void addItems(final Collection<T> items) {
        for (final T item : items) {
            addItem(item);
        }
    }

    @Override
    public void clearItems() {
        synchronized (mQuadTree) {
            mItems.clear();
            mQuadTree.clear();
        }
    }

    private Bounds createBoundsFromSpan(final Point p, final double span) {
        // TODO: Use a span that takes into account the visual size of the
        // marker, not just its
        // LatLng.
        final double halfSpan = span / 2;
        return new Bounds(p.x - halfSpan, p.x + halfSpan, p.y - halfSpan, p.y
                + halfSpan);
    }

    private double distanceSquared(final Point a, final Point b) {
        return ((a.x - b.x) * (a.x - b.x)) + ((a.y - b.y) * (a.y - b.y));
    }

    @Override
    public Set<? extends Cluster<T>> getClusters(final double zoom) {
        final int discreteZoom = (int) zoom;

        final double zoomSpecificSpan = NonHierarchicalDistanceBasedAlgorithm.MAX_DISTANCE_AT_ZOOM
                / Math.pow(2, discreteZoom) / 256;

        final Set<QuadItem<T>> visitedCandidates = new HashSet<QuadItem<T>>();
        final Set<Cluster<T>> results = new HashSet<Cluster<T>>();
        final Map<QuadItem<T>, Double> distanceToCluster = new HashMap<QuadItem<T>, Double>();
        final Map<QuadItem<T>, StaticCluster<T>> itemToCluster = new HashMap<QuadItem<T>, StaticCluster<T>>();

        synchronized (mQuadTree) {
            for (final QuadItem<T> candidate : mItems) {
                if (visitedCandidates.contains(candidate)) {
                    // Candidate is already part of another cluster.
                    continue;
                }

                final Bounds searchBounds = createBoundsFromSpan(
                        candidate.getPoint(), zoomSpecificSpan);
                Collection<QuadItem<T>> clusterItems;
                clusterItems = mQuadTree.search(searchBounds);
                if (clusterItems.size() == 1) {
                    // Only the current marker is in range. Just add the single
                    // item to the results.
                    results.add(candidate);
                    visitedCandidates.add(candidate);
                    distanceToCluster.put(candidate, 0d);
                    continue;
                }
                final StaticCluster<T> cluster = new StaticCluster<T>(
                        candidate.mClusterItem.getPosition());
                results.add(cluster);

                for (final QuadItem<T> clusterItem : clusterItems) {
                    final Double existingDistance = distanceToCluster
                            .get(clusterItem);
                    final double distance = distanceSquared(
                            clusterItem.getPoint(), candidate.getPoint());
                    if (existingDistance != null) {
                        // Item already belongs to another cluster. Check if
                        // it's closer to this cluster.
                        if (existingDistance < distance) {
                            continue;
                        }
                        // Move item to the closer cluster.
                        itemToCluster.get(clusterItem).remove(
                                clusterItem.mClusterItem);
                    }
                    distanceToCluster.put(clusterItem, distance);
                    cluster.add(clusterItem.mClusterItem);
                    itemToCluster.put(clusterItem, cluster);
                }
                visitedCandidates.addAll(clusterItems);
            }
        }
        return results;
    }

    @Override
    public Collection<T> getItems() {
        final List<T> items = new ArrayList<T>();
        synchronized (mQuadTree) {
            for (final QuadItem<T> quadItem : mItems) {
                items.add(quadItem.mClusterItem);
            }
        }
        return items;
    }

    @Override
    public void removeItem(final T item) {
        // TODO: delegate QuadItem#hashCode and QuadItem#equals to its item.
        throw new UnsupportedOperationException(
                "NonHierarchicalDistanceBasedAlgorithm.remove not implemented");
    }
}
